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EARTH SCIENCES
RESEARCH JOURNAL
Earth Sci. Res. J. Vol. 12, No. 2 (December 2008): 181-193
HYDROLOGIC HOMOGENEOUS REGIONS USING MONTHLY
STREAMFLOW IN TURKEY
Ercan Kahya1, Mehmet C. Demirel2 and Osman A. Bég3
1
Prof., Civil Engineering Department.
Hydraulics Division, Istanbul Technical University. Maslak, 34469 Istanbul-Turkey.
Phone: (90) (212) 285 3002 – Fax: (90) (212) 285 65 87 E-mail: [email protected]
2
Department of Water Engineering and Management. University of Twente,
PO Box 217, 7500 AE Enschede, the Netherlands.
Work Tel: (31) 53 489 3911 – Fax: (31) 53 489 35377 E-mail: [email protected]
3
Assoc. Prof., Mechanical Engineering Department
Sheffield Hallam University Sheaf Building, Room 4112 Sheffield, S1 1WB, England, UK
E-mail: [email protected]
ABSTRACT
Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the
characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of
objectively classifying streamflow data into regions encompassing similar streamflow patterns over Turkey.
The performance of three standardization techniques was also tested, and standardizing by range was found
better than standardizing with zero mean and unit variance. Clustering was carried out using Ward’s minimum
variance method which became prominent in managing water resources with squared Euclidean dissimilarity
measures on 80 streamflow stations. The stations have natural flow regimes where no intensive river regulation
had occurred. A general conclusion drawn is that the zones having similar streamflow pattern were not be overlapped well with the conventional climate zones of Turkey; however, they are coherent with the climate zones of
Turkey recently redefined by the cluster analysis to total precipitation data as well as homogenous streamflow
zones of Turkey determined by the rotated principal component analysis. The regional streamflow information
in this study can significantly improve the accuracy of flow predictions in ungauged watersheds.
Key words: Cluster analysis, Ward’s method, streamflow, homogeneous region, regionalization, Turkey
Manuscript receiver: August 18th, 2008.
Accepted for publication: October 10 th, 2008.
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RESUMEN
El análisis de nidos de registros de flujos de corrientes calibrados en regiones homogéneas y robustas es un
instrumento importante para la caracterización de sistemas hidrológicos. En este artículo hemos aplicado este
análisis para clasificar objetivamente datos de flujos de corrientes en una región que comprende patrones
similares en Turquía. El desempeño de las tres técnicas de estandarización probado y estandarizado por rangos,
fue mejor que la estandarización con media cero y varianza 1. El anidamiento se llevó a cabo utilizando el
método de mínima varianza de Ward el cual se torna prominente en el manejo de recursos acuíferos con medidas
de dis-similaridad cuadráticas euclidianas sobre 80 estaciones de flujos de corriente. Las estaciones poseen
regímenes de flujos donde no ha ocurrido regulación intensiva sobre los ríos. Una conclusión general es que las
zonas que tienen patrones similares de flujos de corriente, no fueron bien cubiertas con las zonas climáticas
convencionales de Turquía.
Palabras clave: Análisis racimo, Método de Ward, Flujo de corriente, Región homogénea, regionalización,
Turquía.
1. Introduction
Streamflow characteristics provide information needed
in design of structures built in or along stream channels,
for avoiding flood hazards, for defining the available
water supply and in the large scale provides a useful
tool for extrapolation of hydrological variables and for
the identification of natural flow regimes where intensive river regulation has occurred. Because climate factors, such as precipitation, temperature, sunshine,
humidity, and wind, all affect streamflow but topography, soil characteristics, precipitation and temperature
account for major differences among the river catchments (Haines 1988; Riggs 1985). For instance high
temperature variability generally leads to more potential evaporation so that the water cycle turns in a
warmer environment. Hence the higher content of water vapour in a warmer atmosphere will increase precipitation. But in summer, the streamflow will be
decreased by higher temperatures and higher
evapotranspiration (Stahl 2001).
It is important to document climatic and
hydrologic regionalization in planning water resources systems. This requires similar pattern and
clustering characteristics. In this context, Fovell
(1993) was among the first pioneering studies which
attempted to develop a regionalization for climatic
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variables over the US using monthly temperature
means and precipitation accumulations from 344 climate divisions. Gaffen and Ross (1999) applied a
modified version of eight-cluster solution to analyze
trends in US temperature and humidity.
Stahl (2001) correlated the monthly averages of
the Regional Streamflow Deficiency Index (RDI) series of the 19 European clusters with the NAO index
and noted weak relations. However seasonal correlations were much higher except for the summer season
in northern Europe. In Europe, most rivers show a
strong seasonal regime; therefore, seasonal variability is important to assess the impact of climate
changes on the complex hydrological system (Stahl
2001).
The seasonality of streamflow varies widely from
stream to stream and is influenced mostly by the local
distribution of precipitation, local seasonal cycle of
evaporation demand, timing of snowmelt, travel times
of water from runoff source areas through surface and
subsurface reservoirs and channels to stream gauge,
and human management (Chiang 1996). Dettinger and
Diaz (2000) worked with the global dataset of
monthly streamflow series and pointed out that the
timing and amplitude of streamflow seasonality depends on the local month of maximum precipitation
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and the extent to which precipitation is trapped in
snow and ice at most gauges. Acreman (1986) classified 168 basins in Scotland using Normix multivariate
clustering algorithm. They used logarithmically transformed basin characteristics; area, stream length,
channel slope, stream density, rainfall, soil moisture
deficit, soil type, and lake storage.
In cluster analysis, the choice of variables, clustering technique and dissimilarity measure significantly influence the results (Fovell 1993; Stooksbury
and Micheals 1991). The final groups may or may not
be geographically contiguous. If robust clustering is
done, strong relationship in streamflow properties
(e.g., mean, standard deviation, and correlation of
monthly streamflow) and river basin characteristics
can be determined. These links can be utilized to develop useful streamflow information at ungauged
watersheds featuring similar patterns (Chiang 1996).
Using temperature and precipitation data, some
climate classifications to delineate regions with similar climate conditions in Turkey were previously presented by TürkeÕ (1996) and Ünal et al., (2003). The
former applied a common approach (Thornthwaite
classification method) as a priori definition of a set of
climate types or rules that were then used to classify
climate of Turkey. The latter applied cluster analysis
for the same purpose. Since streamflow is an integrated variable of atmospheric and land processes, it
would be wise to explore clustering schemes from the
hydrological standpoint using nation wide streamflow
network in Turkey. In this study we carry out the
cluster procedures for delineating the geographical
zones having similar monthly streamflow variations.
2. Data and methodology
2.1 Streamflow Data
Our study domain includes 26 river basins across
Turkey (Figure 1). Because of unreliable records we,
however, had to eliminate the basins 2, 10, 11, and 25
from the analysis. Table 1 presents gauging stations
and their basins used in this study. Most of the drainage basins are medium to large size (>1000 km) and
are located in an elevated area (>500m). The maximum flow per unit area can be observed in Antalya
basin as the Eastern Black Sea basin has the highest
precipitation measurements. Turkey is located in
semiarid zone where precipitation is mainly characterized by high spatial and temporal variability.
Readers are referred to Ünal et al., (2003) and Karaca
Figure 1. Locations of streamflow gauging stations used in this study. The boundaries of river basins are shown along with
station ID numbers.
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ERCAN KAHYA, MEHMET C. DEMIREL AND OSMAN ANWAR BÉG
et al. (2000) for a recent review of the general climate
features in Turkey. Monthly streamflow recorded at
80 stations used in this study are compiled by General Directorate of Electrical Power Resources Survey and Development Administration (abbreviated
as EIE). Each streamflow station contains a 31-year
period spanning from 1964 to 1994. Karabörk and
Kahya (1999) and Kahya and Karabörk (2001)
showed that the data set used in this study fulfils the
homogeneity condition at a desirable confidence.
Following suggestion of Arabie et al. (1996),
original streamflow data first were standardized by
the following equation prior to the cluster analysis.
Table 1. Gauging stations used in this study and their locations
Basin No
Name of River Basin
Number of the Gauging Stations’
1
Maritza (Meriç)
101
2
Marmara
-
3
Susurluk
302, 311, 314, 316, 317, 321, 324
4
Northern Aegean
406, 407
5
Gediz
509, 510, 514, 518
6
Small Menderes
601
7
Big Menderes
701, 706, 713
8
Western Mediterranean
808, 809, 812
9
Antalya
902, 912
10
Burdur Lake
-
11
Akarçay
-
12
Sakarya
1203, 1216, 1221, 1222, 1223, 1224, 1226, 1233, 1237, 1242, 1243
13
Western Black Sea
1302, 1307, 1314, 1335
14
Yeþilýrmak
1401, 1402, 1413, 1414, 1418
15
Kýzýlýrmak
1501, 1517, 1524, 1528, 1532, 1535
16
Konya Closed.
1611, 1612
17
Eastern Mediterranean
1708, 1712, 1714
18
Seyhan
1801, 1805, 1818
19
Orontes (Asi)
1905, 1906
20
Ceyhan
2006, 2015
21
Euphrates (Fýrat)
2122, 2124, 2131, 2132, 2145, 2147, 2151
22
Eastern Black Sea
2213, 2218, 2232, 2233
23
Chorokhi (Çoruh)
2304, 2305, 2323
24
Arax (Aras)
2402, 2409
25
Van Lake
-
26
Tigris(Dicle)
2603, 2610, 2612
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Z it =
S it - min ( S it )
max( S it ) - ( S it )
Eq. (1)
where Zit: streamflow index and Sit: monthly
streamflow value at station i in year t (i = 1,..., 80 and
t = 1,2,..., 31).
2.2 Cluster Analysis
Cluster analysis is an unsupervised learning procedure that group names and number of groups are not
known in priori. Classification differs from clustering since it is a supervised learning procedure in
which group names and numbers of groups are
known. Since the purpose of cluster analysis is to organize observed data into meaningful structures, it
combines data objects into groups (clusters) such that
objects belonging to the same cluster are similar as
those belonging to different clusters are dissimilar
(Anderberg 1973; Everitt 1993; Karaca et al. 2000).
To measure the distance between two stations x
and y, the Euclidean distance function, d is frequently
used (Chiang 1996; Gong and Richman 1995) and
expressed as
d = x- y =
n
åx
i
- yi
2
Eq. (2)
i=1
where x and y is the station pair and n is the number
of months. Although there are many other distance
metrics, the Euclidean distance is the most commonly used dissimilarity measure in the clustering algorithms. A literature review provided by Gong and
Richman shows that the majority of investigators
(i.e., 85%) applied this metric in their study. The
Ward’s algorithm and squared Euclidean metric were
selected in this study because this linkage method
aims to join entities or cases into clusters such that
the variance within a cluster is minimized (Everitt,
1993). To be more precise; each case begins as its
own cluster then two clusters are merged if this
merger results in a minimum increase in the error
sum of squares. Readers are referred to Everitt (1993)
and Romesburg (1984) for further details concerning
cluster analysis.
Determination of the appropriate number of
clusters to retain is considered as one of the major unresolved issues in the cluster analysis. In this study,
we applied an informal method which includes an examination of the differences between a conjunction
level in the dendrogram and cutting the dendrogram
when large changes are observed (Everitt, 1993). It is
then possible to define different cluster numbers by
moving the dashed horizontal line up and down in the
graph of dendrogram until achieving a desirable result. Moreover, we applied two other statistics to decide an appropriate number of clusters i.e.
root-mean-square standard deviations (RMSSTD),
pseudo F statistic (Sarle 1983).
4. Results and discussion
A group of 80 stations was analyzed using the hierarchical clustering method described in the preceding
section. An agglomerative clustering method show
which stations or clusters are being clustered together at each step of the analysis procedure. This requires a total of 79 (80-1) steps to converge to one
single cluster. The Ward’s minimum variance
method was applied to the distance matrix constructed from the standardized monthly mean variables. For each variable, the analysis process was
stopped at the 60th (calculated as 80-20) step to detect
variation in the cluster memberships and to get more
consistent clusters. At the beginning of the analysis,
we carried out the cluster procedures up to 20 steps
using both standardized and original variables to see
which type of variables seems to be proper for the
analysis. The 20 steps was the possible reasonable
largest number of cluster (hereafter abbreviated as
NCL). If it was a larger number, it would not be practical to handle the analysis outcomes.
The results for the monthly streamflow variables
were presented in a mapping fashion for the cluster
level of 6 which was selected among possible 20 different cluster levels. This cluster level seemed to account more for compact and reasonable solutions in a
manageable manner and is consistent that of Ünal et
al. (2003). Different colours for each cluster will be
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ERCAN KAHYA, MEHMET C. DEMIREL AND OSMAN ANWAR BÉG
ing the boundary of characteristic mid Turkey
hydroclimatology. For March, the overall pattern
seems somewhere in-between those of January and
February, resembling to the latter’s map for the west
side and to the map of the former for the east side.
Region C of March cluster solution remains unchanged but its designation was changed to Region E
in February solution. Region F includes Konya Plateau in all monthly analysis except January and February months where annual rainfall frequently is less
than 300 mm. In this region, May is generally the
wettest month and July and August are the driest season.
used to demonstrate the analysis results on the maps
afterward.
In addition, we calculated the RMSSTDs and
pseudo F statistics to decide an appropriate number
of clusters and presented their results together with
those of dendrogram in Table 2 for each month. As a
result, all three different techniques suggested us
more or less same number. We considered single
digit for the number of clusters, namely 6, for each
month.
Figure 2 illustrates 6 distinctive clusters, each
showing a hydrologically homogeneous region
across Turkey. For January, four clusters appeared to
be prevailing, mainly having a stripe-like shape extending from north to south. For the coastal areas of
Mediterranean Sea, two clusters come out not only in
this month but also in the February and March, dividing the entire coastal area into the west and east parts.
For February, six clusters emerged almost equally in
size, and each extending more or less from west to
east. In this month, the patterns of streamflow variation in the Marmara and Aegean areas differs each
other as opposed to the case in January in which the
both areas were confined in Region A (Figure 2a).
The entire Black Sea coast lines were represented by
a single cluster, namely Region B. Southern part of
Kizilirmak basin and Konya Closed basin together
were identified by Region C in February, represent-
Similar detailed evaluations can be made for the
remaining months (figures 3, 4 and 5); however, we
will introduce common and striking features of the
map patterns after this point. There is immense similarity between cluster solutions of January and
April, both belonging to different season. In the
same context, the map pattern of May, in general, is
said to be a replication of that of May and April, implying that spring months demonstrate nearly common cluster pattern. Region F in the cluster solution
of April is often appears in most months, composing
of the basins 16, 17, 18 and 20, and the stations
2124, 2145 and 2131. This cluster region was also
noted by (Kahya and Kalayc1 2002; Kahya et al.,
2008). They used an alternative approach that is the
Table 2. Examination of the number of clusters in monthly domain
January
Suggested
RMSSTD
6
Pseudo F
5
Dendrogram
6
February
Selected
Suggested
8
8
Pseudo F
4
Dendrogram
6
8
Suggested
5
6
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Suggested
Selected
8
Suggested
8
5
6
Selected
6
8
Suggested
Selected
7
6
7
Suggested
8
5
6
Selected
5
8
6
December
Selected
5
8
Suggested
8
November
6
8
Selected
6
October
Selected
Suggested
June
6
6
5
8
May
4
September
4
8
Selected
6
August
Selected
Suggested
April
7
6
Suggested
6
Selected
7
July
RMSSTD
March
Suggested
Selected
4
8
6
6
8
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Figure 2. Homogeneous streamflow regions for the months: (a) January, (b) February, and (c) March
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ERCAN KAHYA, MEHMET C. DEMIREL AND OSMAN ANWAR BÉG
Figure 3: Homogeneous streamflow regions for the months: (a) April, (b) May, and (c) June
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Figure 4. Homogeneous streamflow regions for the months: (a) July, (b) August, and (c) September
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ERCAN KAHYA, MEHMET C. DEMIREL AND OSMAN ANWAR BÉG
Figure 5: Homogeneous streamflow regions for the months: (a) October, (b) November, and (c) December
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rotated principal component analysis. Region E
(Figure 3b) is another spatially widespread cluster
covering eastern Turkey.
months January, June, and November with a
large size. It might be concluded that it is very
stable throughout the year.
The comparison of all twelve monthly cluster patterns together led us to draw the following features for
hydrologic regionalization of Turkish streamflow patterns.
(viii) The geographical extent defined by the Region
E cluster in Figure 2c shows a temporal consistency during the months March, partially in
June, July, August, and with much larger area
coverage in December.
(i)
(ii)
Region C in the cluster solution of October may
be the most disaggregated scheme which includes the basins 17, 19 and 22.
Figure 4 designates that there is no significant
change in the map patterns of July, August, and
even September.
(iii) The eastern Black Sea basin where current national energy politics mainly rely on this basin
was divided into two sub-regions in the analysis of the following months: January, April,
May, June and July.
(iv)
In two monthly patterns (i.e., June and December), there exists a single cluster (i.e., Region F
in Figure 3 and Region C in Figure 4, respectively) occupying almost half of the entire
country.
(v)
In particular during the months January and
April, streamflow stations in the Marmara and
Aegean area (marked as Region A in the both
cases) reveal common variation modes. For the
remaining months, the both regions were included by separate clusters.
(vi) The area described by Region C cluster during
the months March, April, May, June, July, and
August sequentially appears to delineate the
same geographical extent (referring Kizilirmak
and Yesilirmak basins), indicating a noticeable
temporal persistency as well.
(vii) The cluster defined by Region F constantly
emerges with almost same geographical extent
(referring Antalya, Konya Closed, Eastern
Mediterranean, Ceyhan, and some parts of Firat
basins) during the months March, April, May,
July, August, and September. It should be
noted that this cluster also comes out during the
(ix) The cluster Region D defined in Figure 3b, implying similar streamflow variation mode in the
Marmara and western Black Sea areas, appears
during the months may, August, September,
and with little larger area in October. This cluster includes the basins Meric, Western Black
Sea, and some parts of Kizilirmak basins.
Isik and Singh (2008) also applied cluster analysis to the same domain to demonstrate hydrologic
regionalization. They included 1410 stations from 26
river basins having at least 5 year data. They used
flow duration curves in the analysis to estimate
streamflow values at desired ungagued sites after the
homogeneous regions were defined by cluster analysis methods. The number of clusters was chosen by
hierarchical methods and homogenous regions were
delineated by k-means method. They used standard
Euclidean distance instead of squared Euclidean that
we used in our application as a measure of dissimilarity. Our results for the number of cluster is consistent
with those of Isik and Singh (2008). Readers are referred to our recent discussions regarding methodological aspects of the cluster analysis (Demirel et
al., 2008a; Demirel et al., 2008b).
Explanations concerning climatological reasoning are out of the scope of this investigation. However, it is very important to document the patterns of
monthly precipitation and temperature variables in
similar manner, and to relate to the results presented
here.
5. Conclusions
Within a river basin, hydrologic processes are integrated into streamflow characteristics; thus,
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ERCAN KAHYA, MEHMET C. DEMIREL AND OSMAN ANWAR BÉG
streamflow data provide a natural filter for precipitation data (Piechota et al., 1997) and was preferred
unique study-variable in this study. We then set the
goals of this investigation as to determine streamflow
zones of Turkey, as objectively as possible, and evaluate the stability of solutions based on the feedbacks
obtained from variable pre-processing, choosing the
best algorithm, and NCL. We specifically considered
two measures: the Euclidean and squared Euclidean.
Ward’s minimum variance method was decided to
yield acceptable results in our solutions. Ünal et al.,
(2003) redefined 7 climatic zones for Turkey as quite
different boundaries than conventional classification. Hence in this paper, the number of homogenous
cluster is chosen as 6 for month domain according to
visual inspection of the dendrogram and two statistics of clusters.
The initial data consisted of monthly average
streamflow resulting in a data matrix of 80-station x
31-variable.
(a)
The Ward’s method with squared Euclidean
was found more effective in producing homogenous cluster schemes when comparing to the
other HCA methods.
(b)
Using monthly patterns seem to be favourable
in regard to defining streamflow regions.
(c)
Standardization by range is superior to the
other techniques.
The outcomes of this study were in a good agreement and relation with earlier studies conducted for, in
general, Turkish hydrologic and climatological surface variables. For example, precipitation and
streamflow variables showed some significant downward trends in western Turkey (Partal and Kahya
2006; Kahya and Kalayci 2002; Kahya and Kalayci
2004) where we usually assigned Region A in this
study. Ünal et al., (2003) who developed a
regionalization of climate in Turkey using cluster
analysis, pointed out eight regions of similar climate
pattern. Some of their regions are quite similar to those
found here. However the zones having similar
streamflow pattern appeared not overlapped well with
the conventional climate zones of Turkey. Although
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river drainage basins were not a major impetus for the
climate divisions in Turkey, which are based primarily
on political boundaries with limited attention given to
topography and other factors, streamflow variable is
strongly recommended for inclusion in the dataset
used for climate clustering studies.
6. References
Acreman, M. C., and Sinclair, C. D. (1986). Classification of drainage basins according to their physical characteristics; an application for flood
frequency analysis in Scotland. Journal of Hydrology, 84(3/4), 365-380.
Anderberg, M. R. (1973). Cluster Analysis for Applications, Academic Press, New York NY and
London.
Arabie, D., Hubert, L. J., and De Soete, G. (1996).
Clustering and Classification, World Scientific
Publ., River Edge, NJ.
Chiang, S. M. (1996). Hydrologic Regionalization
for the Estimation of Streamflows at Ungauged
Sites Based On Time Series Analysis and
Multivariate Statistical Analysis., Syracuse University, New York.
Demirel, M. C., Kahya, E., and Rivera, D. (2008a). Discussion of “Hydrologic Regionalization of Watersheds in Turkey” by Sabahattin Isik; and Vijay P.
Singh in ASCE Journal of Hydrologic Engineering, Sep 2008, Vol. 13, No. 9, pp.824-834. DOI:
10.1061/(asce) 1084-0699(2008) 13:9(824) (accepted for publication).
Demirel, M.C., Kahya, E. and Rivera, D. (2008b).
Discussion of “Clustering On Dissimilarity Representations for Detecting Mislabelled Seismic
Signals at Nevado Del Ruiz Volcano” by
Mauricio Orozco-Alzate, and César Germán
Castellanos-Domínguez, Earth Sciences Research Journal (in press).
Dettinger, M. D., and Diaz, H. F. (2000). Global
characteristics of streamflow and variability.
Journal of Hydrometeorology, 1, 289-310.
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Everitt, B. (1993). Cluster Analysis. 3rd edn. ,
Halsted Press, Division of Wiley, New York.
Fovell, R. G., and Fovell M.Y.C. (1993). Climate
Zones of the Conterminous United States Defined Using Cluster Analysis. J.Climate, 6,
2103-2135.
Gaffen, D. J., and Ross, R. J. (1999). Climatology
and Trends of U.S. Surface Humidity and Temperature. Journal of Climate, 12(3), 811-828.
Gong, X., and Richman, M. B. (1995). On the Application of Cluster Analysis to Growing Season
Precipitation Data in North America East of the
Rockies. . J. Climate, 8, 897-931.
Haines, A. T. a. F., B.L. and McMahon, T.A. (1988).
A global classification of river regimes. Applied
Geography, 8(4), 255-272.
Isik, S., and Singh, V. P. (2008). Hydrologic
regionalization of watersheds in Turkey. Journal
Of Hydrologic Engineering, 13(9), 824-834.
Karaca, M., Deniz, A., and Tayanc, M. (2000). Cyclone track variability over Turkey in association
with regional climate. International Journal of
Climatology, 20, 122-136.
Partal, T. and Kahya, E. (2006). Trend Analysis of
Turkish Precipitation Patterns. Hydrological
Processes, 20 (9), 2011-2026.
Piechota, T. C., Dracup, J. A., and Fovell, R. G.
(1997). Western US streamflow and atmospheric circulation patterns during El
Nino-Southern Oscillation. Journal of Hydrology, 201, 249-271.
Riggs, H. C. (1985). Streamflow characteristics,
Elsevier, Amsterdam.
Romesburg, H. C. (1984). Cluster Analysis for Researchers, Lifetime Learning Publications,
Belmont, California.
Sarle, W. S. (1983). Cubic clustering criterion. SAS
Institute, Cary, NC.
Kahya, E., and Kalaycý, S. (2002). Streamflow Variability in Turkey. AGROENVIRON 2002, Cairo,
Egypt.
Stahl, K. (2001). Hydrological Drought: a study
across Europe, Albert-Ludwigs-Universität,
Freiburg.
Kahya, E., Kalaycý, S., and Piechota, T.C. (2008).
Streamflow Regionalization: Case Study of Turkey. Journal of Hydrologic Engineering, 13(4),
205-214.
Stooksbury, D. E., and Micheals, P. J. (1991).
Cluster Analysis of Southern U.S. Climate Stations. Theoretical and Applied Climatology,
44, 143-150.
Karabörk, Ç., and Kahya, E. (1999). Multivariate Stochastic Modeling of Streamflows in the Sakarya
Basin (in Turkish). Turkish Journal of Engineering and Environmental Sciences, 23(2), 133-147.
Türkeþ, M. (1996). Spatial and Temporal Analysis
of Annual Rainfall Variations in Turkey. International Journal of Climatology, 16,
1057-1076.
Kahya, E., and Karabörk, M.Ç. (2001). The Analysis
of El Nino and La Nina Signals in Streamflows
of Turkey. International Journal of Climatology,
21(10), 1231-1250.
Ünal, Y., Kindap, T., and Karaca, M. (2003). Redefining the climate zones of Turkey using cluster
analysis. International Journal of Climatology,
23(9), 1045-1055.
Kahya, E. and Kalaycý, S. (2004). Trend Analysis of
Streamflow in Turkey. Journal of Hydrology,
289, 128-144.
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